Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations500
Missing cells500
Missing cells (%)6.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.7 KiB
Average record size in memory120.3 B

Variable types

Text4
Categorical1
Numeric9
Unsupported1

Alerts

BasePay is highly overall correlated with OvertimePayHigh correlation
OvertimePay is highly overall correlated with BasePayHigh correlation
TotalPay is highly overall correlated with TotalPayBenefitsHigh correlation
TotalPayBenefits is highly overall correlated with TotalPayHigh correlation
age is highly overall correlated with car purchase amountHigh correlation
car purchase amount is highly overall correlated with ageHigh correlation
Benefits has 500 (100.0%) missing valuesMissing
customer e-mail has unique valuesUnique
credit card debt has unique valuesUnique
net worth has unique valuesUnique
car purchase amount has unique valuesUnique
Benefits is an unsupported type, check if it needs cleaning or further analysisUnsupported
OvertimePay has 169 (33.8%) zerosZeros
OtherPay has 55 (11.0%) zerosZeros

Reproduction

Analysis started2024-11-06 04:08:38.821740
Analysis finished2024-11-06 04:08:59.586429
Duration20.76 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct498
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:00.576688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length22
Median length17
Mean length12.804
Min length3

Characters and Unicode

Total characters6402
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique496 ?
Unique (%)99.2%

Sample

1st rowMartina Avila
2nd rowHarlan Barnes
3rd rowNaomi Rodriquez
4th rowJade Cunningham
5th rowCedric Leach
ValueCountFrequency (%)
d 12
 
1.1%
q 11
 
1.0%
f 11
 
1.0%
u 10
 
0.9%
l 10
 
0.9%
a 10
 
0.9%
p 9
 
0.8%
h 9
 
0.8%
m 8
 
0.7%
s 8
 
0.7%
Other values (706) 1001
91.1%
2024-11-06T06:09:01.726815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 612
 
9.6%
599
 
9.4%
a 561
 
8.8%
n 443
 
6.9%
r 416
 
6.5%
l 334
 
5.2%
o 308
 
4.8%
i 307
 
4.8%
s 204
 
3.2%
. 200
 
3.1%
Other values (45) 2418
37.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6402
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 612
 
9.6%
599
 
9.4%
a 561
 
8.8%
n 443
 
6.9%
r 416
 
6.5%
l 334
 
5.2%
o 308
 
4.8%
i 307
 
4.8%
s 204
 
3.2%
. 200
 
3.1%
Other values (45) 2418
37.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6402
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 612
 
9.6%
599
 
9.4%
a 561
 
8.8%
n 443
 
6.9%
r 416
 
6.5%
l 334
 
5.2%
o 308
 
4.8%
i 307
 
4.8%
s 204
 
3.2%
. 200
 
3.1%
Other values (45) 2418
37.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6402
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 612
 
9.6%
599
 
9.4%
a 561
 
8.8%
n 443
 
6.9%
r 416
 
6.5%
l 334
 
5.2%
o 308
 
4.8%
i 307
 
4.8%
s 204
 
3.2%
. 200
 
3.1%
Other values (45) 2418
37.8%
Distinct82
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:02.244691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length50
Median length44
Mean length25.298
Min length5

Characters and Unicode

Total characters12649
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)7.2%

Sample

1st rowGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY
2nd rowCAPTAIN III (POLICE DEPARTMENT)
3rd rowCAPTAIN III (POLICE DEPARTMENT)
4th rowWIRE ROPE CABLE MAINTENANCE MECHANIC
5th rowDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)
ValueCountFrequency (%)
department 182
 
11.5%
fire 134
 
8.5%
iii 104
 
6.6%
police 104
 
6.6%
captain 70
 
4.4%
chief 63
 
4.0%
firefighter 55
 
3.5%
lieutenant 53
 
3.4%
deputy 39
 
2.5%
supervisor 37
 
2.3%
Other values (103) 737
46.7%
2024-11-06T06:09:02.948684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 1552
12.3%
E 1500
11.9%
1078
 
8.5%
T 1073
 
8.5%
R 933
 
7.4%
A 863
 
6.8%
N 811
 
6.4%
P 650
 
5.1%
C 531
 
4.2%
S 528
 
4.2%
Other values (20) 3130
24.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12649
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1552
12.3%
E 1500
11.9%
1078
 
8.5%
T 1073
 
8.5%
R 933
 
7.4%
A 863
 
6.8%
N 811
 
6.4%
P 650
 
5.1%
C 531
 
4.2%
S 528
 
4.2%
Other values (20) 3130
24.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12649
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1552
12.3%
E 1500
11.9%
1078
 
8.5%
T 1073
 
8.5%
R 933
 
7.4%
A 863
 
6.8%
N 811
 
6.4%
P 650
 
5.1%
C 531
 
4.2%
S 528
 
4.2%
Other values (20) 3130
24.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12649
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1552
12.3%
E 1500
11.9%
1078
 
8.5%
T 1073
 
8.5%
R 933
 
7.4%
A 863
 
6.8%
N 811
 
6.4%
P 650
 
5.1%
C 531
 
4.2%
S 528
 
4.2%
Other values (20) 3130
24.7%

customer e-mail
Text

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:03.410599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length58
Median length41
Mean length27.606
Min length10

Characters and Unicode

Total characters13803
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique500 ?
Unique (%)100.0%

Sample

1st rowcubilia.Curae.Phasellus@quisaccumsanconvallis.edu
2nd roweu.dolor@diam.co.uk
3rd rowvulputate.mauris.sagittis@ametconsectetueradipiscing.co.uk
4th rowmalesuada@dignissim.com
5th rowfelis.ullamcorper.viverra@egetmollislectus.net
ValueCountFrequency (%)
felis.ullamcorper.viverra@egetmollislectus.net 1
 
0.2%
camaron.marla@hotmail.com 1
 
0.2%
cubilia.curae.phasellus@quisaccumsanconvallis.edu 1
 
0.2%
eu.dolor@diam.co.uk 1
 
0.2%
phasellus.fermentum@dictumplacerataugue.net 1
 
0.2%
ipsum.phasellus@egestasblanditnam.edu 1
 
0.2%
iaculis.enim@nislelementum.edu 1
 
0.2%
porttitor.tellus@elitelit.org 1
 
0.2%
felis.nulla@mi.ca 1
 
0.2%
curabitur.egestas.nunc@fermentumfermentum.ca 1
 
0.2%
Other values (490) 490
98.0%
2024-11-06T06:09:04.144012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1383
 
10.0%
u 1129
 
8.2%
. 1067
 
7.7%
i 1053
 
7.6%
a 963
 
7.0%
s 941
 
6.8%
t 899
 
6.5%
n 744
 
5.4%
c 730
 
5.3%
o 723
 
5.2%
Other values (29) 4171
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13803
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1383
 
10.0%
u 1129
 
8.2%
. 1067
 
7.7%
i 1053
 
7.6%
a 963
 
7.0%
s 941
 
6.8%
t 899
 
6.5%
n 744
 
5.4%
c 730
 
5.3%
o 723
 
5.2%
Other values (29) 4171
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13803
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1383
 
10.0%
u 1129
 
8.2%
. 1067
 
7.7%
i 1053
 
7.6%
a 963
 
7.0%
s 941
 
6.8%
t 899
 
6.5%
n 744
 
5.4%
c 730
 
5.3%
o 723
 
5.2%
Other values (29) 4171
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13803
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1383
 
10.0%
u 1129
 
8.2%
. 1067
 
7.7%
i 1053
 
7.6%
a 963
 
7.0%
s 941
 
6.8%
t 899
 
6.5%
n 744
 
5.4%
c 730
 
5.3%
o 723
 
5.2%
Other values (29) 4171
30.2%
Distinct211
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:04.693964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length44
Median length32
Mean length10.266
Min length4

Characters and Unicode

Total characters5133
Distinct characters59
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)11.8%

Sample

1st rowBulgaria
2nd rowBelize
3rd rowAlgeria
4th rowCook Islands
5th rowBrazil
ValueCountFrequency (%)
islands 35
 
4.7%
and 30
 
4.0%
saint 21
 
2.8%
guinea 12
 
1.6%
united 10
 
1.3%
island 10
 
1.3%
french 8
 
1.1%
south 8
 
1.1%
republic 6
 
0.8%
israel 6
 
0.8%
Other values (250) 602
80.5%
2024-11-06T06:09:05.632949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 721
14.0%
n 426
 
8.3%
i 413
 
8.0%
e 386
 
7.5%
r 282
 
5.5%
248
 
4.8%
l 233
 
4.5%
o 232
 
4.5%
t 230
 
4.5%
s 223
 
4.3%
Other values (49) 1739
33.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5133
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 721
14.0%
n 426
 
8.3%
i 413
 
8.0%
e 386
 
7.5%
r 282
 
5.5%
248
 
4.8%
l 233
 
4.5%
o 232
 
4.5%
t 230
 
4.5%
s 223
 
4.3%
Other values (49) 1739
33.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5133
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 721
14.0%
n 426
 
8.3%
i 413
 
8.0%
e 386
 
7.5%
r 282
 
5.5%
248
 
4.8%
l 233
 
4.5%
o 232
 
4.5%
t 230
 
4.5%
s 223
 
4.3%
Other values (49) 1739
33.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5133
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 721
14.0%
n 426
 
8.3%
i 413
 
8.0%
e 386
 
7.5%
r 282
 
5.5%
248
 
4.8%
l 233
 
4.5%
o 232
 
4.5%
t 230
 
4.5%
s 223
 
4.3%
Other values (49) 1739
33.9%

gender
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
253 
0
247 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 253
50.6%
0 247
49.4%

Length

2024-11-06T06:09:05.893404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-06T06:09:06.144008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 253
50.6%
0 247
49.4%

Most occurring characters

ValueCountFrequency (%)
1 253
50.6%
0 247
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 253
50.6%
0 247
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 253
50.6%
0 247
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 253
50.6%
0 247
49.4%

age
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.224
Minimum20
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:06.426942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile33
Q141
median46
Q352
95-th percentile59.05
Maximum70
Range50
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.9903389
Coefficient of variation (CV)0.17286126
Kurtosis-0.07296831
Mean46.224
Median Absolute Deviation (MAD)5
Skewness0.0080821527
Sum23112
Variance63.845515
MonotonicityNot monotonic
2024-11-06T06:09:06.698031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
43 34
 
6.8%
51 29
 
5.8%
42 26
 
5.2%
48 25
 
5.0%
47 25
 
5.0%
40 24
 
4.8%
44 23
 
4.6%
45 23
 
4.6%
53 20
 
4.0%
50 19
 
3.8%
Other values (33) 252
50.4%
ValueCountFrequency (%)
20 1
 
0.2%
22 1
 
0.2%
25 1
 
0.2%
27 1
 
0.2%
28 1
 
0.2%
29 2
 
0.4%
30 3
 
0.6%
31 1
 
0.2%
32 9
1.8%
33 10
2.0%
ValueCountFrequency (%)
70 2
 
0.4%
65 1
 
0.2%
64 1
 
0.2%
63 6
 
1.2%
62 5
 
1.0%
61 5
 
1.0%
60 5
 
1.0%
59 6
 
1.2%
58 7
1.4%
57 17
3.4%

BasePay
Real number (ℝ)

HIGH CORRELATION 

Distinct371
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151066.63
Minimum25400
Maximum294580.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:07.143779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum25400
5-th percentile87357.37
Q1116316.36
median140546.9
Q3188314.51
95-th percentile220158.61
Maximum294580.02
Range269180.02
Interquartile range (IQR)71998.153

Descriptive statistics

Standard deviation44402.93
Coefficient of variation (CV)0.29392944
Kurtosis-0.29836746
Mean151066.63
Median Absolute Deviation (MAD)34612.24
Skewness0.33005319
Sum75533315
Variance1.9716202 × 109
MonotonicityNot monotonic
2024-11-06T06:09:07.410037image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105934.66 10
 
2.0%
105934.64 10
 
2.0%
105934.65 10
 
2.0%
123105 9
 
1.8%
140546.88 8
 
1.6%
192379.6 6
 
1.2%
188341.6 6
 
1.2%
188341.62 6
 
1.2%
122404.98 5
 
1.0%
105934.67 5
 
1.0%
Other values (361) 425
85.0%
ValueCountFrequency (%)
25400 1
0.2%
60373.82 1
0.2%
61232 1
0.2%
63625.16 1
0.2%
63811.18 1
0.2%
63878.03 1
0.2%
73754.05 1
0.2%
75870.3 1
0.2%
76298.77 1
0.2%
76424.2 1
0.2%
ValueCountFrequency (%)
294580.02 1
0.2%
285262 1
0.2%
271329.03 1
0.2%
268604.57 1
0.2%
261717.6 1
0.2%
257510.59 1
0.2%
257510.48 1
0.2%
257510.44 1
0.2%
256576.96 1
0.2%
256470.41 1
0.2%

OvertimePay
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct329
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34445.374
Minimum0
Maximum245131.88
Zeros169
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:07.767341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24079.42
Q364893.007
95-th percentile102135.6
Maximum245131.88
Range245131.88
Interquartile range (IQR)64893.007

Descriptive statistics

Standard deviation37322.38
Coefficient of variation (CV)1.0835237
Kurtosis1.0205521
Mean34445.374
Median Absolute Deviation (MAD)24079.42
Skewness0.94254468
Sum17222687
Variance1.39296 × 109
MonotonicityNot monotonic
2024-11-06T06:09:08.092384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 169
33.8%
1109.1 3
 
0.6%
7437 2
 
0.4%
245131.88 1
 
0.2%
106088.18 1
 
0.2%
56120.71 1
 
0.2%
9737 1
 
0.2%
8601 1
 
0.2%
89062.9 1
 
0.2%
86362.68 1
 
0.2%
Other values (319) 319
63.8%
ValueCountFrequency (%)
0 169
33.8%
377.21 1
 
0.2%
444.6 1
 
0.2%
619.4 1
 
0.2%
743.34 1
 
0.2%
880.16 1
 
0.2%
1068.45 1
 
0.2%
1109.1 3
 
0.6%
1131.28 1
 
0.2%
1134.34 1
 
0.2%
ValueCountFrequency (%)
245131.88 1
0.2%
139102.95 1
0.2%
135159.38 1
0.2%
126778.88 1
0.2%
126725.82 1
0.2%
119951.72 1
0.2%
119407.7 1
0.2%
119397.26 1
0.2%
118949.93 1
0.2%
115239.98 1
0.2%

OtherPay
Real number (ℝ)

ZEROS 

Distinct437
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25762.908
Minimum0
Maximum400184.25
Zeros55
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:08.375503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110161.333
median17475.785
Q328726.082
95-th percentile87151.375
Maximum400184.25
Range400184.25
Interquartile range (IQR)18564.75

Descriptive statistics

Standard deviation33597.479
Coefficient of variation (CV)1.3041027
Kurtosis34.962625
Mean25762.908
Median Absolute Deviation (MAD)9715.91
Skewness4.5162553
Sum12881454
Variance1.1287906 × 109
MonotonicityNot monotonic
2024-11-06T06:09:08.675695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
 
11.0%
12000 3
 
0.6%
1080.96 3
 
0.6%
21055.17 2
 
0.4%
17542.29 2
 
0.4%
32080.36 2
 
0.4%
10000 2
 
0.4%
15365.22 2
 
0.4%
189082.74 1
 
0.2%
23865 1
 
0.2%
Other values (427) 427
85.4%
ValueCountFrequency (%)
0 55
11.0%
114.37 1
 
0.2%
237 1
 
0.2%
408 1
 
0.2%
702 1
 
0.2%
760.4 1
 
0.2%
773.7 1
 
0.2%
819.03 1
 
0.2%
837.79 1
 
0.2%
841.15 1
 
0.2%
ValueCountFrequency (%)
400184.25 1
0.2%
198306.9 1
0.2%
189082.74 1
0.2%
182234.59 1
0.2%
142094.49 1
0.2%
139279.69 1
0.2%
137811.38 1
0.2%
135684.25 1
0.2%
134426.14 1
0.2%
133695.76 1
0.2%

Benefits
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing500
Missing (%)100.0%
Memory size4.0 KiB

TotalPay
Real number (ℝ)

HIGH CORRELATION 

Distinct491
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211274.91
Minimum185724.5
Maximum567595.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:08.993935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum185724.5
5-th percentile186645.93
Q1192696.08
median200595.26
Q3217400.32
95-th percentile268109.21
Maximum567595.43
Range381870.93
Interquartile range (IQR)24704.243

Descriptive statistics

Standard deviation34040.951
Coefficient of variation (CV)0.1611216
Kurtosis41.287891
Mean211274.91
Median Absolute Deviation (MAD)10259.05
Skewness4.9554096
Sum1.0563746 × 108
Variance1.1587864 × 109
MonotonicityDecreasing
2024-11-06T06:09:09.276464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192379.6 4
 
0.8%
214988.68 3
 
0.6%
185724.5 3
 
0.6%
220788.36 2
 
0.4%
196886.55 2
 
0.4%
538909.28 1
 
0.2%
335279.91 1
 
0.2%
332343.61 1
 
0.2%
326373.19 1
 
0.2%
316285.74 1
 
0.2%
Other values (481) 481
96.2%
ValueCountFrequency (%)
185724.5 3
0.6%
185760.92 1
 
0.2%
185951.55 1
 
0.2%
186049.41 1
 
0.2%
186075.92 1
 
0.2%
186105.02 1
 
0.2%
186191.94 1
 
0.2%
186231.74 1
 
0.2%
186297.21 1
 
0.2%
186341.79 1
 
0.2%
ValueCountFrequency (%)
567595.43 1
0.2%
538909.28 1
0.2%
335279.91 1
0.2%
332343.61 1
0.2%
326373.19 1
0.2%
316285.74 1
0.2%
315981.05 1
0.2%
307899.46 1
0.2%
303427.55 1
0.2%
302377.73 1
0.2%

TotalPayBenefits
Real number (ℝ)

HIGH CORRELATION 

Distinct491
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211274.91
Minimum185724.5
Maximum567595.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:09.577031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum185724.5
5-th percentile186645.93
Q1192696.08
median200595.26
Q3217400.32
95-th percentile268109.21
Maximum567595.43
Range381870.93
Interquartile range (IQR)24704.243

Descriptive statistics

Standard deviation34040.951
Coefficient of variation (CV)0.1611216
Kurtosis41.287891
Mean211274.91
Median Absolute Deviation (MAD)10259.05
Skewness4.9554096
Sum1.0563746 × 108
Variance1.1587864 × 109
MonotonicityDecreasing
2024-11-06T06:09:09.866903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192379.6 4
 
0.8%
214988.68 3
 
0.6%
185724.5 3
 
0.6%
220788.36 2
 
0.4%
196886.55 2
 
0.4%
538909.28 1
 
0.2%
335279.91 1
 
0.2%
332343.61 1
 
0.2%
326373.19 1
 
0.2%
316285.74 1
 
0.2%
Other values (481) 481
96.2%
ValueCountFrequency (%)
185724.5 3
0.6%
185760.92 1
 
0.2%
185951.55 1
 
0.2%
186049.41 1
 
0.2%
186075.92 1
 
0.2%
186105.02 1
 
0.2%
186191.94 1
 
0.2%
186231.74 1
 
0.2%
186297.21 1
 
0.2%
186341.79 1
 
0.2%
ValueCountFrequency (%)
567595.43 1
0.2%
538909.28 1
0.2%
335279.91 1
0.2%
332343.61 1
0.2%
326373.19 1
0.2%
316285.74 1
0.2%
315981.05 1
0.2%
307899.46 1
0.2%
303427.55 1
0.2%
302377.73 1
0.2%

credit card debt
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9607.645
Minimum100
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:10.163117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile3848.5025
Q17397.5158
median9655.0356
Q311798.867
95-th percentile15193.805
Maximum20000
Range19900
Interquartile range (IQR)4401.3517

Descriptive statistics

Standard deviation3489.188
Coefficient of variation (CV)0.36316787
Kurtosis0.099148574
Mean9607.645
Median Absolute Deviation (MAD)2220.4134
Skewness-0.063724404
Sum4803822.5
Variance12174433
MonotonicityNot monotonic
2024-11-06T06:09:10.443157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9391.341628 1
 
0.2%
11609.38091 1
 
0.2%
9572.957136 1
 
0.2%
11160.35506 1
 
0.2%
10373.00856 1
 
0.2%
14297.25366 1
 
0.2%
10769.75059 1
 
0.2%
9141.668545 1
 
0.2%
11561.07365 1
 
0.2%
6596.01369 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
100 1
0.2%
594.8049491 1
0.2%
632.0528524 1
0.2%
640.045378 1
0.2%
861.8166529 1
0.2%
893.2353408 1
0.2%
921.5340234 1
0.2%
1065.607589 1
0.2%
1696.989764 1
0.2%
1726.809885 1
0.2%
ValueCountFrequency (%)
20000 1
0.2%
19692.91262 1
0.2%
18693.14652 1
0.2%
18361.24915 1
0.2%
17870.63765 1
0.2%
17805.57607 1
0.2%
17462.07506 1
0.2%
17452.92179 1
0.2%
17142.41332 1
0.2%
16978.52745 1
0.2%

net worth
Real number (ℝ)

UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean431475.71
Minimum20000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:10.744085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20000
5-th percentile158464.61
Q1299824.2
median426750.12
Q3557324.48
95-th percentile720603.9
Maximum1000000
Range980000
Interquartile range (IQR)257500.28

Descriptive statistics

Standard deviation173536.76
Coefficient of variation (CV)0.40219357
Kurtosis-0.33406563
Mean431475.71
Median Absolute Deviation (MAD)128997.55
Skewness0.13975525
Sum2.1573786 × 108
Variance3.0115006 × 1010
MonotonicityNot monotonic
2024-11-06T06:09:11.009571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
462946.4924 1
 
0.2%
238961.2505 1
 
0.2%
530973.9078 1
 
0.2%
638467.1773 1
 
0.2%
620355.2658 1
 
0.2%
247421.9185 1
 
0.2%
276466.6203 1
 
0.2%
531840.3342 1
 
0.2%
421891.846 1
 
0.2%
266939.1746 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
20000 1
0.2%
27888.74525 1
0.2%
48620.32123 1
0.2%
53366.13861 1
0.2%
59630.07789 1
0.2%
62149.94034 1
0.2%
69821.6376 1
0.2%
74257.82785 1
0.2%
85520.85055 1
0.2%
97706.89181 1
0.2%
ValueCountFrequency (%)
1000000 1
0.2%
891439.8761 1
0.2%
856287.1522 1
0.2%
854283.5574 1
0.2%
853913.8532 1
0.2%
830430.3692 1
0.2%
819002.1748 1
0.2%
811594.0392 1
0.2%
805075.5197 1
0.2%
793986.6155 1
0.2%

car purchase amount
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44209.799
Minimum9000
Maximum80000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2024-11-06T06:09:11.443074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum9000
5-th percentile27623.505
Q137629.896
median43997.783
Q351254.71
95-th percentile62070.498
Maximum80000
Range71000
Interquartile range (IQR)13624.813

Descriptive statistics

Standard deviation10773.179
Coefficient of variation (CV)0.24368305
Kurtosis0.22723801
Mean44209.799
Median Absolute Deviation (MAD)6741.5855
Skewness-0.030790505
Sum22104900
Variance1.1606138 × 108
MonotonicityNot monotonic
2024-11-06T06:09:11.918976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45107.22566 1
 
0.2%
35321.45877 1
 
0.2%
45115.52566 1
 
0.2%
42925.70921 1
 
0.2%
55377.87697 1
 
0.2%
45015.67953 1
 
0.2%
21471.11367 1
 
0.2%
53049.44567 1
 
0.2%
53110.88052 1
 
0.2%
36517.70996 1
 
0.2%
Other values (490) 490
98.0%
ValueCountFrequency (%)
9000 1
0.2%
10092.22509 1
0.2%
12536.93842 1
0.2%
12895.71468 1
0.2%
17584.56963 1
0.2%
19525.29827 1
0.2%
19553.2739 1
0.2%
20653.21409 1
0.2%
21471.11367 1
0.2%
22091.11839 1
0.2%
ValueCountFrequency (%)
80000 1
0.2%
70878.29664 1
0.2%
70598.96768 1
0.2%
69669.47402 1
0.2%
68925.09447 1
0.2%
68678.4352 1
0.2%
67422.36313 1
0.2%
67120.89878 1
0.2%
67092.23276 1
0.2%
66888.93694 1
0.2%

Interactions

2024-11-06T06:08:56.534301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:40.088605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:42.137735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:43.973816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:45.840538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:47.933238image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:50.200360image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:52.130142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:54.065080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:56.747454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:40.347814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:42.334739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:44.163936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:46.038883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:48.141531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:50.399514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:52.329740image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:54.298335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:56.968313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:40.537589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:42.527684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:44.341025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:46.235908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:48.472699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:50.606842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:52.532828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:54.568031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:57.179460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:40.716791image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:42.737931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:44.524680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:46.437549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:48.674826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:50.803714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:52.740266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:54.889821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:57.414923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:40.939235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:42.947232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:44.737152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:46.656397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:49.065439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:51.022667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:52.969208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:55.144969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:57.634863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:41.138431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:43.151375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:45.002399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:46.880559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:49.290561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:51.253033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:53.199956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:55.481402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:57.933277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:41.540432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:43.339772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:45.214241image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:47.101567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:49.521680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:51.463162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:53.419281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:55.702487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:58.204782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:41.743529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:43.563783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:45.432057image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:47.384547image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:49.750331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:51.686680image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:53.640748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:56.115880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:58.477032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:41.937316image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:43.767140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:45.620883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:47.698326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:49.957154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:51.900441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:53.849766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-06T06:08:56.319538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-06T06:09:12.361653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BasePayOtherPayOvertimePayTotalPayTotalPayBenefitsagecar purchase amountcredit card debtgendernet worth
BasePay1.000-0.408-0.7710.1840.1840.0190.018-0.0130.0000.033
OtherPay-0.4081.0000.1640.1890.189-0.030-0.0530.0820.0670.022
OvertimePay-0.7710.1641.0000.1260.126-0.011-0.0180.0210.081-0.027
TotalPay0.1840.1890.1261.0001.0000.0520.0070.1060.1010.029
TotalPayBenefits0.1840.1890.1261.0001.0000.0520.0070.1060.1010.029
age0.019-0.030-0.0110.0520.0521.0000.6240.0200.0000.016
car purchase amount0.018-0.053-0.0180.0070.0070.6241.0000.0250.0000.464
credit card debt-0.0130.0820.0210.1060.1060.0200.0251.0000.007-0.047
gender0.0000.0670.0810.1010.1010.0000.0000.0071.0000.121
net worth0.0330.022-0.0270.0290.0290.0160.464-0.0470.1211.000

Missing values

2024-11-06T06:08:58.826692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-06T06:08:59.310129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer nameJobTitlecustomer e-mailcountrygenderageBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitscredit card debtnet worthcar purchase amount
0Martina AvilaGENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITYcubilia.Curae.Phasellus@quisaccumsanconvallis.eduBulgaria042167411.180.00400184.25NaN567595.43567595.4311609.380910238961.250535321.45877
1Harlan BarnesCAPTAIN III (POLICE DEPARTMENT)eu.dolor@diam.co.ukBelize041155966.02245131.88137811.38NaN538909.28538909.289572.957136530973.907845115.52566
2Naomi RodriquezCAPTAIN III (POLICE DEPARTMENT)vulputate.mauris.sagittis@ametconsectetueradipiscing.co.ukAlgeria143212739.13106088.1816452.60NaN335279.91335279.9111160.355060638467.177342925.70921
3Jade CunninghamWIRE ROPE CABLE MAINTENANCE MECHANICmalesuada@dignissim.comCook Islands15877916.0056120.71198306.90NaN332343.61332343.6114426.164850548599.052467422.36313
4Cedric LeachDEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)felis.ullamcorper.viverra@egetmollislectus.netBrazil157134401.609737.00182234.59NaN326373.19326373.195358.712177560304.067155915.46248
5Carla HesterASSISTANT DEPUTY CHIEF IImi@Aliquamerat.eduLiberia157118602.008601.00189082.74NaN316285.74316285.7414179.472440428485.360456611.99784
6Griffin RiveraBATTALION CHIEF, (FIRE DEPARTMENT)vehicula@at.co.ukSyria14792492.0189062.90134426.14NaN315981.05315981.055958.460188326373.181228925.70549
7Orli CaseyDEPUTY DIRECTOR OF INVESTMENTSnunc.est.mollis@Suspendissetristiqueneque.co.ukCzech Republic150256576.960.0051322.50NaN307899.46307899.4610985.696560629312.404147434.98265
8Marny ObrienBATTALION CHIEF, (FIRE DEPARTMENT)Phasellus@sedsemegestas.orgArmenia047176932.6486362.6840132.23NaN303427.55303427.553440.823799630059.027448013.61410
9Rhonda ChavezCHIEF OF DEPARTMENT, (FIRE DEPARTMENT)nec@nuncest.comSomalia143285262.000.0017115.73NaN302377.73302377.7312884.078680476643.354438189.50601
customer nameJobTitlecustomer e-mailcountrygenderageBasePayOvertimePayOtherPayBenefitsTotalPayTotalPayBenefitscredit card debtnet worthcar purchase amount
490JonahLIEUTENANT III (POLICE DEPARTMENT)augue@risusNuncac.co.ukMyanmar146149044.7734003.883183.09NaN186231.74186231.7410711.444720316128.400241352.47071
491MerrillNURSE MANAGERdolor.sit@turpisIn.comEgypt150171044.100.0015147.84NaN186191.94186191.9410072.482980294506.084452785.16947
492NolanCAPTAIN, FIRE SUPPRESSIONDonec.at@neccursus.co.ukLatvia055129252.0130839.7426013.27NaN186105.02186105.029831.184792523680.769960117.67886
493WinterFIREFIGHTERegestas.urna.justo@maurissagittis.eduWallis and Futuna043105934.6462182.7317958.55NaN186075.92186075.9213308.879320349588.560847760.66427
494RigelLIEUTENANT, FIRE DEPARTMENTegestas.blandit.Nam@semvitaealiquam.comSao Tome and Principe052123013.0047172.4615863.95NaN186049.41186049.416736.751680665099.139064188.26862
495WalterTRANSIT SUPERVISORligula@Cumsociis.caNepal04187384.6097729.16837.79NaN185951.55185951.556995.902524541670.101648901.44342
496VannaLIEUTENANT, FIRE DEPARTMENTCum.sociis.natoque@Sedmolestie.eduZimbabwe138123105.0038790.9223865.00NaN185760.92185760.9212301.456790360419.098831491.41457
497PearlDEPUTY DIRECTOR Vpenatibus.et@massanonante.comPhilippines154185724.500.000.00NaN185724.50185724.5010611.606860764531.320364147.28888
498NellMANAGER VIIIQuisque.varius@arcuVivamussit.netBotswana159185724.500.000.00NaN185724.50185724.5014013.034510337826.638245442.15353
499MarlaDEPUTY DIRECTOR VCamaron.marla@hotmail.commarlal147185724.500.000.00NaN185724.50185724.509391.341628462946.492445107.22566